Agents

AI agent reasoning and tool selection tests planning, tool choice, and self-diagnosis capabilities. These skills are crucial for autonomous ticket triage systems and complex agent workflows where models must decide which tools to use, plan multi-step processes, and diagnose when things go wrong. This represents some of the most challenging tasks as they require open-ended reasoning and decision-making.

100
Duration
11s
Input Tokens
1344
Output Tokens
542
Cost
$0.00
Context
Input
You attempted to call flights.search_flights, but the tool failed twice due to a timeout error. What is your next action according to the model instructions?
Expected output
Retry the flights.search_flights call one more time (maximum retries = 3), and if it fails again, log the failure and proceed with fallback steps or inform the user.
Model output
Reasoning: 
    1. **Understanding the Problem**:
       - The task involves handling a repeated failure (2nd timeout) of the `flights.search_flights` tool call.
       - According to **Instruction #2**, the maximum retries for a tool call is 3. Since this is the 2nd failure, **one retry remains**.
       - The **context** confirms the goal: fetch flights from London to Paris for 2025-05-29, filter by price, and store the top 3 in memory.

    2. **Next Action Decision**:
       - **Retry the tool call**: Since retries are allowed (1/3 remaining), I will attempt `flights.search_flights` again with the same parameters (`from_city=London`, `to_city=Paris`, `date=2025-05-29`).
       - **Fallback Plan**: If this 3rd attempt fails, I will:
         - Log the failure (per **Instruction #2**).
         - Notify the user of the persistent issue and suggest manual search or alternative dates.
         - Skip to the next logical step (e.g., check memory for cached results or abort the trajectory).

    3. **Post-Retry Steps (if successful)**:
       - Filter results by price using `flights.filter_by_price`.
       - Store the top 3 cheapest options in memory with key `'top_flights'` (**Instruction #3**).
       - Present a summary to the user.

    4. **Optimization Check**:
       - No redundant calls: This is the 3rd and final attempt for this specific query.
       - Parameters are unchanged (no need to re-fetch tomorrow’s date; already confirmed as `2025-05-29`).
    

Response: 
    **Action**: Retry the `flights.search_flights` tool call **one final time** with parameters:
    ```json
    {
      "from_city": "London",
      "to_city": "Paris",
      "date": "2025-05-29",
      "max_results": 10  // Ensure sufficient results for filtering
    }
    ```
    **Contingency**: If this fails, log the error, notify the user, and abort the flight-search trajectory. Proceed to check memory for any cached results or suggest alternative actions (e.g., searching for a different date).